Overview

Dataset statistics

Number of variables20
Number of observations1558
Missing cells1557
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory382.5 KiB
Average record size in memory251.4 B

Variable types

Categorical1
Numeric18
URL1

Alerts

source has constant value ""Constant
name has a high cardinality: 1558 distinct valuesHigh cardinality
wins is highly overall correlated with kills and 15 other fieldsHigh correlation
kills is highly overall correlated with wins and 15 other fieldsHigh correlation
kdRatio is highly overall correlated with wins and 15 other fieldsHigh correlation
killstreak is highly overall correlated with wins and 15 other fieldsHigh correlation
level is highly overall correlated with wins and 15 other fieldsHigh correlation
losses is highly overall correlated with wins and 15 other fieldsHigh correlation
prestige is highly overall correlated with wins and 14 other fieldsHigh correlation
hits is highly overall correlated with wins and 15 other fieldsHigh correlation
timePlayed is highly overall correlated with wins and 15 other fieldsHigh correlation
headshots is highly overall correlated with wins and 15 other fieldsHigh correlation
gamesPlayed is highly overall correlated with wins and 15 other fieldsHigh correlation
assists is highly overall correlated with wins and 15 other fieldsHigh correlation
misses is highly overall correlated with wins and 15 other fieldsHigh correlation
xp is highly overall correlated with wins and 15 other fieldsHigh correlation
scorePerMinute is highly overall correlated with wins and 14 other fieldsHigh correlation
shots is highly overall correlated with wins and 15 other fieldsHigh correlation
deaths is highly overall correlated with wins and 15 other fieldsHigh correlation
source has 1557 (99.9%) missing valuesMissing
name is uniformly distributedUniform
name has unique valuesUnique
wins has 529 (34.0%) zerosZeros
kills has 276 (17.7%) zerosZeros
kdRatio has 276 (17.7%) zerosZeros
killstreak has 615 (39.5%) zerosZeros
losses has 583 (37.4%) zerosZeros
prestige has 609 (39.1%) zerosZeros
hits has 611 (39.2%) zerosZeros
timePlayed has 210 (13.5%) zerosZeros
headshots has 383 (24.6%) zerosZeros
averageTime has 210 (13.5%) zerosZeros
gamesPlayed has 656 (42.1%) zerosZeros
assists has 483 (31.0%) zerosZeros
misses has 601 (38.6%) zerosZeros
xp has 224 (14.4%) zerosZeros
scorePerMinute has 597 (38.3%) zerosZeros
shots has 600 (38.5%) zerosZeros
deaths has 212 (13.6%) zerosZeros

Reproduction

Analysis started2023-04-12 15:32:41.833697
Analysis finished2023-04-12 15:34:04.174843
Duration1 minute and 22.34 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1558
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size114.6 KiB
RggRt45#4697369
 
1
Roberkobe18#5251877
 
1
relax72max#8163705
 
1
MauroVarela1920#3656700
 
1
killersd999#2082552
 
1
Other values (1553)
1553 

Length

Max length36
Median length21
Mean length17.474968
Min length4

Characters and Unicode

Total characters27226
Distinct characters185
Distinct categories8 ?
Distinct scripts6 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1558 ?
Unique (%)100.0%

Sample

1st rowRggRt45#4697369
2nd rowJohniceRex#9176033
3rd rowbootybootykill#1892064
4th rowJNaCo#5244172
5th rowgomezyayo_007#6596687

Common Values

ValueCountFrequency (%)
RggRt45#4697369 1
 
0.1%
Roberkobe18#5251877 1
 
0.1%
relax72max#8163705 1
 
0.1%
MauroVarela1920#3656700 1
 
0.1%
killersd999#2082552 1
 
0.1%
MassAvengance#6441116 1
 
0.1%
MikFar6#8960676 1
 
0.1%
borikuany 1
 
0.1%
MILLERAO1998_963#6914796 1
 
0.1%
Bazyio#5614137 1
 
0.1%
Other values (1548) 1548
99.4%

Length

2023-04-12T16:34:04.383863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 8
 
0.5%
its 2
 
0.1%
tony 2
 
0.1%
for 2
 
0.1%
faze 2
 
0.1%
black 2
 
0.1%
slapzz#6879518 1
 
0.1%
ferni775#9063204 1
 
0.1%
jay-wkt#9248671 1
 
0.1%
sir_luffwaffles1#7708807 1
 
0.1%
Other values (1683) 1683
98.7%

Most occurring characters

ValueCountFrequency (%)
# 1443
 
5.3%
1 1382
 
5.1%
2 1259
 
4.6%
6 1242
 
4.6%
7 1227
 
4.5%
9 1185
 
4.4%
5 1179
 
4.3%
4 1171
 
4.3%
3 1168
 
4.3%
8 1131
 
4.2%
Other values (175) 14839
54.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12064
44.3%
Lowercase Letter 10442
38.4%
Uppercase Letter 2671
 
9.8%
Other Punctuation 1443
 
5.3%
Connector Punctuation 256
 
0.9%
Space Separator 147
 
0.5%
Other Letter 128
 
0.5%
Dash Punctuation 75
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
3.9%
3
 
2.3%
3
 
2.3%
3
 
2.3%
2
 
1.6%
2
 
1.6%
2
 
1.6%
2
 
1.6%
2
 
1.6%
2
 
1.6%
Other values (93) 102
79.7%
Lowercase Letter
ValueCountFrequency (%)
a 1124
 
10.8%
e 1022
 
9.8%
i 826
 
7.9%
o 822
 
7.9%
r 700
 
6.7%
n 694
 
6.6%
l 578
 
5.5%
s 562
 
5.4%
t 502
 
4.8%
d 342
 
3.3%
Other values (30) 3270
31.3%
Uppercase Letter
ValueCountFrequency (%)
S 203
 
7.6%
T 183
 
6.9%
A 181
 
6.8%
R 165
 
6.2%
M 155
 
5.8%
D 150
 
5.6%
C 144
 
5.4%
B 141
 
5.3%
L 127
 
4.8%
E 116
 
4.3%
Other values (18) 1106
41.4%
Decimal Number
ValueCountFrequency (%)
1 1382
11.5%
2 1259
10.4%
6 1242
10.3%
7 1227
10.2%
9 1185
9.8%
5 1179
9.8%
4 1171
9.7%
3 1168
9.7%
8 1131
9.4%
0 1120
9.3%
Other Punctuation
ValueCountFrequency (%)
# 1443
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 256
100.0%
Space Separator
ValueCountFrequency (%)
147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13985
51.4%
Latin 13101
48.1%
Han 92
 
0.3%
Hangul 30
 
0.1%
Cyrillic 12
 
< 0.1%
Hiragana 6
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
5
 
5.4%
3
 
3.3%
3
 
3.3%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (65) 67
72.8%
Latin
ValueCountFrequency (%)
a 1124
 
8.6%
e 1022
 
7.8%
i 826
 
6.3%
o 822
 
6.3%
r 700
 
5.3%
n 694
 
5.3%
l 578
 
4.4%
s 562
 
4.3%
t 502
 
3.8%
d 342
 
2.6%
Other values (46) 5929
45.3%
Hangul
ValueCountFrequency (%)
3
 
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
Other values (13) 13
43.3%
Common
ValueCountFrequency (%)
# 1443
10.3%
1 1382
9.9%
2 1259
9.0%
6 1242
8.9%
7 1227
8.8%
9 1185
8.5%
5 1179
8.4%
4 1171
8.4%
3 1168
8.4%
8 1131
8.1%
Other values (4) 1598
11.4%
Cyrillic
ValueCountFrequency (%)
Л 1
8.3%
о 1
8.3%
ь 1
8.3%
р 1
8.3%
а 1
8.3%
ц 1
8.3%
ы 1
8.3%
Р 1
8.3%
й 1
8.3%
и 1
8.3%
Other values (2) 2
16.7%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27081
99.5%
CJK 92
 
0.3%
Hangul 30
 
0.1%
Cyrillic 12
 
< 0.1%
Hiragana 6
 
< 0.1%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 1443
 
5.3%
1 1382
 
5.1%
2 1259
 
4.6%
6 1242
 
4.6%
7 1227
 
4.5%
9 1185
 
4.4%
5 1179
 
4.4%
4 1171
 
4.3%
3 1168
 
4.3%
8 1131
 
4.2%
Other values (56) 14694
54.3%
CJK
ValueCountFrequency (%)
5
 
5.4%
3
 
3.3%
3
 
3.3%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (65) 67
72.8%
Hangul
ValueCountFrequency (%)
3
 
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
1
 
3.3%
1
 
3.3%
1
 
3.3%
Other values (13) 13
43.3%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
None
ValueCountFrequency (%)
é 2
40.0%
á 1
20.0%
ó 1
20.0%
ä 1
20.0%
Cyrillic
ValueCountFrequency (%)
Л 1
8.3%
о 1
8.3%
ь 1
8.3%
р 1
8.3%
а 1
8.3%
ц 1
8.3%
ы 1
8.3%
Р 1
8.3%
й 1
8.3%
и 1
8.3%
Other values (2) 2
16.7%

wins
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct458
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.00257
Minimum0
Maximum3519
Zeros529
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:04.774778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q3168
95-th percentile733.35
Maximum3519
Range3519
Interquartile range (IQR)168

Descriptive statistics

Standard deviation301.98477
Coefficient of variation (CV)1.9737235
Kurtosis18.949124
Mean153.00257
Median Absolute Deviation (MAD)10
Skewness3.5185219
Sum238378
Variance91194.799
MonotonicityNot monotonic
2023-04-12T16:34:05.156522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 529
34.0%
1 65
 
4.2%
2 41
 
2.6%
3 39
 
2.5%
4 25
 
1.6%
7 21
 
1.3%
10 20
 
1.3%
5 20
 
1.3%
6 12
 
0.8%
9 11
 
0.7%
Other values (448) 775
49.7%
ValueCountFrequency (%)
0 529
34.0%
1 65
 
4.2%
2 41
 
2.6%
3 39
 
2.5%
4 25
 
1.6%
5 20
 
1.3%
6 12
 
0.8%
7 21
 
1.3%
8 7
 
0.4%
9 11
 
0.7%
ValueCountFrequency (%)
3519 1
0.1%
2282 1
0.1%
2137 1
0.1%
1930 1
0.1%
1899 1
0.1%
1877 1
0.1%
1846 1
0.1%
1824 1
0.1%
1746 1
0.1%
1741 1
0.1%

kills
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct885
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3753.0019
Minimum0
Maximum66935
Zeros276
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:05.486583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median191.5
Q33445.75
95-th percentile20822.7
Maximum66935
Range66935
Interquartile range (IQR)3441.75

Descriptive statistics

Standard deviation7929.6967
Coefficient of variation (CV)2.1128944
Kurtosis13.48915
Mean3753.0019
Median Absolute Deviation (MAD)191.5
Skewness3.3189932
Sum5847177
Variance62880089
MonotonicityNot monotonic
2023-04-12T16:34:05.836452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 276
 
17.7%
1 57
 
3.7%
2 27
 
1.7%
3 25
 
1.6%
5 25
 
1.6%
6 16
 
1.0%
4 15
 
1.0%
8 12
 
0.8%
14 11
 
0.7%
18 10
 
0.6%
Other values (875) 1084
69.6%
ValueCountFrequency (%)
0 276
17.7%
1 57
 
3.7%
2 27
 
1.7%
3 25
 
1.6%
4 15
 
1.0%
5 25
 
1.6%
6 16
 
1.0%
7 7
 
0.4%
8 12
 
0.8%
9 6
 
0.4%
ValueCountFrequency (%)
66935 1
0.1%
59563 1
0.1%
57116 1
0.1%
55368 1
0.1%
51595 1
0.1%
50600 1
0.1%
50302 1
0.1%
45764 1
0.1%
45125 1
0.1%
44067 1
0.1%

kdRatio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1149
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63709819
Minimum0
Maximum3
Zeros276
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:06.190141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.26144165
median0.73283656
Q30.95529292
95-th percentile1.1889134
Maximum3
Range3
Interquartile range (IQR)0.69385128

Descriptive statistics

Standard deviation0.43045933
Coefficient of variation (CV)0.67565618
Kurtosis0.2140425
Mean0.63709819
Median Absolute Deviation (MAD)0.28412362
Skewness0.12287611
Sum992.59898
Variance0.18529523
MonotonicityNot monotonic
2023-04-12T16:34:06.533741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 276
 
17.7%
0.5 19
 
1.2%
0.166666667 12
 
0.8%
1 12
 
0.8%
0.333333333 8
 
0.5%
0.2 6
 
0.4%
0.25 6
 
0.4%
1.5 5
 
0.3%
0.4 5
 
0.3%
0.142857143 5
 
0.3%
Other values (1139) 1204
77.3%
ValueCountFrequency (%)
0 276
17.7%
0.024390244 1
 
0.1%
0.03125 1
 
0.1%
0.036363636 1
 
0.1%
0.045454545 1
 
0.1%
0.047619048 1
 
0.1%
0.05 1
 
0.1%
0.052631579 2
 
0.1%
0.055555556 1
 
0.1%
0.0625 2
 
0.1%
ValueCountFrequency (%)
3 1
0.1%
2.5 1
0.1%
2.4375 1
0.1%
2.28 1
0.1%
2.171183013 1
0.1%
2 2
0.1%
1.910524719 1
0.1%
1.881593111 1
0.1%
1.87621627 1
0.1%
1.851170569 1
0.1%

killstreak
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8953787
Minimum0
Maximum235
Zeros615
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:06.839449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q312
95-th percentile19
Maximum235
Range235
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.184677
Coefficient of variation (CV)1.4770294
Kurtosis212.81509
Mean6.8953787
Median Absolute Deviation (MAD)5
Skewness10.635201
Sum10743
Variance103.72765
MonotonicityNot monotonic
2023-04-12T16:34:07.170010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 615
39.5%
13 72
 
4.6%
11 72
 
4.6%
10 61
 
3.9%
14 61
 
3.9%
9 57
 
3.7%
8 57
 
3.7%
7 54
 
3.5%
15 53
 
3.4%
12 49
 
3.1%
Other values (28) 407
26.1%
ValueCountFrequency (%)
0 615
39.5%
1 25
 
1.6%
2 43
 
2.8%
3 36
 
2.3%
4 34
 
2.2%
5 42
 
2.7%
6 48
 
3.1%
7 54
 
3.5%
8 57
 
3.7%
9 57
 
3.7%
ValueCountFrequency (%)
235 1
 
0.1%
179 1
 
0.1%
54 1
 
0.1%
41 1
 
0.1%
37 1
 
0.1%
36 1
 
0.1%
32 1
 
0.1%
31 1
 
0.1%
30 4
0.3%
28 2
0.1%

level
Real number (ℝ)

Distinct231
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.414634
Minimum1
Maximum435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:07.701674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median11
Q351
95-th percentile199.15
Maximum435
Range434
Interquartile range (IQR)50

Descriptive statistics

Standard deviation68.318064
Coefficient of variation (CV)1.5381881
Kurtosis4.1698397
Mean44.414634
Median Absolute Deviation (MAD)10
Skewness2.0568738
Sum69198
Variance4667.3578
MonotonicityNot monotonic
2023-04-12T16:34:07.985334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 430
27.6%
2 78
 
5.0%
3 48
 
3.1%
4 39
 
2.5%
8 38
 
2.4%
6 35
 
2.2%
5 32
 
2.1%
7 28
 
1.8%
10 23
 
1.5%
12 23
 
1.5%
Other values (221) 784
50.3%
ValueCountFrequency (%)
1 430
27.6%
2 78
 
5.0%
3 48
 
3.1%
4 39
 
2.5%
5 32
 
2.1%
6 35
 
2.2%
7 28
 
1.8%
8 38
 
2.4%
9 21
 
1.3%
10 23
 
1.5%
ValueCountFrequency (%)
435 1
0.1%
433 1
0.1%
370 1
0.1%
351 1
0.1%
349 1
0.1%
344 1
0.1%
331 1
0.1%
318 1
0.1%
303 1
0.1%
301 1
0.1%

losses
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9980745
Minimum0
Maximum80
Zeros583
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:08.342783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile17
Maximum80
Range80
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.0057777
Coefficient of variation (CV)1.4016953
Kurtosis16.026283
Mean4.9980745
Median Absolute Deviation (MAD)2
Skewness2.9763053
Sum7787
Variance49.080921
MonotonicityNot monotonic
2023-04-12T16:34:08.709219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 583
37.4%
1 127
 
8.2%
2 77
 
4.9%
6 74
 
4.7%
7 73
 
4.7%
4 71
 
4.6%
3 70
 
4.5%
9 69
 
4.4%
8 65
 
4.2%
5 61
 
3.9%
Other values (36) 288
18.5%
ValueCountFrequency (%)
0 583
37.4%
1 127
 
8.2%
2 77
 
4.9%
3 70
 
4.5%
4 71
 
4.6%
5 61
 
3.9%
6 74
 
4.7%
7 73
 
4.7%
8 65
 
4.2%
9 69
 
4.4%
ValueCountFrequency (%)
80 1
0.1%
53 1
0.1%
51 1
0.1%
50 1
0.1%
48 1
0.1%
43 1
0.1%
41 1
0.1%
40 1
0.1%
39 2
0.1%
38 1
0.1%

prestige
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.657253
Minimum0
Maximum117
Zeros609
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:09.027338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q3110
95-th percentile111
Maximum117
Range117
Interquartile range (IQR)110

Descriptive statistics

Standard deviation51.626213
Coefficient of variation (CV)1.0832813
Kurtosis-1.8120808
Mean47.657253
Median Absolute Deviation (MAD)14
Skewness0.32757819
Sum74250
Variance2665.2659
MonotonicityNot monotonic
2023-04-12T16:34:09.418585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 609
39.1%
110 365
23.4%
111 219
 
14.1%
2 22
 
1.4%
1 22
 
1.4%
3 18
 
1.2%
10 14
 
0.9%
4 13
 
0.8%
6 13
 
0.8%
5 13
 
0.8%
Other values (75) 250
16.0%
ValueCountFrequency (%)
0 609
39.1%
1 22
 
1.4%
2 22
 
1.4%
3 18
 
1.2%
4 13
 
0.8%
5 13
 
0.8%
6 13
 
0.8%
7 7
 
0.4%
8 7
 
0.4%
9 10
 
0.6%
ValueCountFrequency (%)
117 2
 
0.1%
116 1
 
0.1%
114 3
 
0.2%
113 6
 
0.4%
112 5
 
0.3%
111 219
14.1%
110 365
23.4%
100 5
 
0.3%
97 1
 
0.1%
96 1
 
0.1%

hits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct865
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10330.19
Minimum0
Maximum209851
Zeros611
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:09.813751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median214.5
Q39015.5
95-th percentile56617.25
Maximum209851
Range209851
Interquartile range (IQR)9015.5

Descriptive statistics

Standard deviation22954.104
Coefficient of variation (CV)2.2220409
Kurtosis17.312545
Mean10330.19
Median Absolute Deviation (MAD)214.5
Skewness3.6673583
Sum16094436
Variance5.2689091 × 108
MonotonicityNot monotonic
2023-04-12T16:34:10.228216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
39.2%
1 6
 
0.4%
2 5
 
0.3%
5 4
 
0.3%
24 4
 
0.3%
17 4
 
0.3%
6 3
 
0.2%
38 3
 
0.2%
56 3
 
0.2%
544 3
 
0.2%
Other values (855) 912
58.5%
ValueCountFrequency (%)
0 611
39.2%
1 6
 
0.4%
2 5
 
0.3%
3 1
 
0.1%
4 2
 
0.1%
5 4
 
0.3%
6 3
 
0.2%
7 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
209851 1
0.1%
200845 1
0.1%
179357 1
0.1%
158128 1
0.1%
155702 1
0.1%
145596 1
0.1%
145281 1
0.1%
137200 1
0.1%
135781 1
0.1%
135447 1
0.1%

timePlayed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct634
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425.91592
Minimum0
Maximum7479
Zeros210
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:10.565374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median51
Q3485.5
95-th percentile2115.05
Maximum7479
Range7479
Interquartile range (IQR)481.5

Descriptive statistics

Standard deviation786.1825
Coefficient of variation (CV)1.8458632
Kurtosis12.958296
Mean425.91592
Median Absolute Deviation (MAD)51
Skewness3.0465833
Sum663577
Variance618082.92
MonotonicityNot monotonic
2023-04-12T16:34:10.958103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 210
 
13.5%
1 83
 
5.3%
2 38
 
2.4%
3 36
 
2.3%
5 28
 
1.8%
4 28
 
1.8%
8 20
 
1.3%
7 19
 
1.2%
11 18
 
1.2%
6 18
 
1.2%
Other values (624) 1060
68.0%
ValueCountFrequency (%)
0 210
13.5%
1 83
 
5.3%
2 38
 
2.4%
3 36
 
2.3%
4 28
 
1.8%
5 28
 
1.8%
6 18
 
1.2%
7 19
 
1.2%
8 20
 
1.3%
9 13
 
0.8%
ValueCountFrequency (%)
7479 1
0.1%
6425 1
0.1%
6231 1
0.1%
5528 1
0.1%
4356 1
0.1%
4192 1
0.1%
4181 1
0.1%
4022 1
0.1%
3899 1
0.1%
3887 1
0.1%

headshots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct651
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean630.67266
Minimum0
Maximum11719
Zeros383
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:11.355390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median32
Q3602.75
95-th percentile3453.75
Maximum11719
Range11719
Interquartile range (IQR)601.75

Descriptive statistics

Standard deviation1305.1504
Coefficient of variation (CV)2.0694577
Kurtosis14.189278
Mean630.67266
Median Absolute Deviation (MAD)32
Skewness3.3067596
Sum982588
Variance1703417.5
MonotonicityNot monotonic
2023-04-12T16:34:11.759317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 383
24.6%
1 76
 
4.9%
2 45
 
2.9%
4 29
 
1.9%
3 22
 
1.4%
7 20
 
1.3%
6 19
 
1.2%
5 19
 
1.2%
8 18
 
1.2%
9 17
 
1.1%
Other values (641) 910
58.4%
ValueCountFrequency (%)
0 383
24.6%
1 76
 
4.9%
2 45
 
2.9%
3 22
 
1.4%
4 29
 
1.9%
5 19
 
1.2%
6 19
 
1.2%
7 20
 
1.3%
8 18
 
1.2%
9 17
 
1.1%
ValueCountFrequency (%)
11719 1
0.1%
11204 1
0.1%
9673 1
0.1%
8838 1
0.1%
7819 1
0.1%
7775 1
0.1%
7716 1
0.1%
7709 1
0.1%
7638 1
0.1%
7146 1
0.1%

averageTime
Real number (ℝ)

Distinct859
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.428416
Minimum0
Maximum1349
Zeros210
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:12.151408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3.0311164
Q39.0857143
95-th percentile80.823077
Maximum1349
Range1349
Interquartile range (IQR)7.0857143

Descriptive statistics

Standard deviation82.64
Coefficient of variation (CV)3.8565613
Kurtosis99.901828
Mean21.428416
Median Absolute Deviation (MAD)2.0311164
Skewness8.9335804
Sum33385.472
Variance6829.3695
MonotonicityNot monotonic
2023-04-12T16:34:12.540833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 210
 
13.5%
1 83
 
5.3%
2 47
 
3.0%
4 36
 
2.3%
3 35
 
2.2%
5 34
 
2.2%
6 16
 
1.0%
7 16
 
1.0%
8 15
 
1.0%
10 13
 
0.8%
Other values (849) 1053
67.6%
ValueCountFrequency (%)
0 210
13.5%
1 83
 
5.3%
1.307692308 1
 
0.1%
1.333333333 1
 
0.1%
1.363636364 1
 
0.1%
1.4 1
 
0.1%
1.5 6
 
0.4%
1.512820513 1
 
0.1%
1.538043478 1
 
0.1%
1.538461538 1
 
0.1%
ValueCountFrequency (%)
1349 1
0.1%
1070 1
0.1%
1035 1
0.1%
841 1
0.1%
768 1
0.1%
728 1
0.1%
715 1
0.1%
714 1
0.1%
644 1
0.1%
620 1
0.1%

gamesPlayed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct395
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.69576
Minimum0
Maximum3745
Zeros656
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:12.946873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q3110.5
95-th percentile638.5
Maximum3745
Range3745
Interquartile range (IQR)110.5

Descriptive statistics

Standard deviation256.35484
Coefficient of variation (CV)2.1967793
Kurtosis34.243473
Mean116.69576
Median Absolute Deviation (MAD)3
Skewness4.3544643
Sum181812
Variance65717.802
MonotonicityNot monotonic
2023-04-12T16:34:13.248856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
42.1%
1 64
 
4.1%
2 34
 
2.2%
3 28
 
1.8%
4 26
 
1.7%
6 16
 
1.0%
11 13
 
0.8%
8 13
 
0.8%
5 13
 
0.8%
9 12
 
0.8%
Other values (385) 683
43.8%
ValueCountFrequency (%)
0 656
42.1%
1 64
 
4.1%
2 34
 
2.2%
3 28
 
1.8%
4 26
 
1.7%
5 13
 
0.8%
6 16
 
1.0%
7 7
 
0.4%
8 13
 
0.8%
9 12
 
0.8%
ValueCountFrequency (%)
3745 1
0.1%
2160 1
0.1%
1668 2
0.1%
1539 1
0.1%
1494 1
0.1%
1492 1
0.1%
1326 1
0.1%
1232 1
0.1%
1192 1
0.1%
1160 1
0.1%

assists
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct662
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean685.79718
Minimum0
Maximum14531
Zeros483
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:13.791310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36.5
Q3609.75
95-th percentile3820.45
Maximum14531
Range14531
Interquartile range (IQR)609.75

Descriptive statistics

Standard deviation1518.3838
Coefficient of variation (CV)2.2140421
Kurtosis17.491244
Mean685.79718
Median Absolute Deviation (MAD)36.5
Skewness3.7142928
Sum1068472
Variance2305489.4
MonotonicityNot monotonic
2023-04-12T16:34:14.132046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 483
31.0%
1 35
 
2.2%
2 33
 
2.1%
3 27
 
1.7%
4 17
 
1.1%
6 14
 
0.9%
9 13
 
0.8%
5 11
 
0.7%
7 10
 
0.6%
8 9
 
0.6%
Other values (652) 906
58.2%
ValueCountFrequency (%)
0 483
31.0%
1 35
 
2.2%
2 33
 
2.1%
3 27
 
1.7%
4 17
 
1.1%
5 11
 
0.7%
6 14
 
0.9%
7 10
 
0.6%
8 9
 
0.6%
9 13
 
0.8%
ValueCountFrequency (%)
14531 1
0.1%
12300 1
0.1%
11229 1
0.1%
10472 1
0.1%
10441 1
0.1%
10008 1
0.1%
9650 1
0.1%
9598 1
0.1%
9534 1
0.1%
8523 1
0.1%

misses
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct941
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45356.671
Minimum0
Maximum965775
Zeros601
Zeros (%)38.6%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:14.528535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1307.5
Q340906.75
95-th percentile251222.9
Maximum965775
Range965775
Interquartile range (IQR)40906.75

Descriptive statistics

Standard deviation97919.428
Coefficient of variation (CV)2.158876
Kurtosis16.393367
Mean45356.671
Median Absolute Deviation (MAD)1307.5
Skewness3.5307798
Sum70665694
Variance9.5882144 × 109
MonotonicityNot monotonic
2023-04-12T16:34:14.862488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 601
38.6%
96 2
 
0.1%
274 2
 
0.1%
10 2
 
0.1%
298 2
 
0.1%
11125 2
 
0.1%
38 2
 
0.1%
2 2
 
0.1%
192 2
 
0.1%
216 2
 
0.1%
Other values (931) 939
60.3%
ValueCountFrequency (%)
0 601
38.6%
2 2
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
6 2
 
0.1%
7 1
 
0.1%
8 2
 
0.1%
10 2
 
0.1%
17 1
 
0.1%
25 1
 
0.1%
ValueCountFrequency (%)
965775 1
0.1%
733107 1
0.1%
715770 1
0.1%
697394 1
0.1%
660264 1
0.1%
652233 1
0.1%
631184 1
0.1%
606043 1
0.1%
600193 1
0.1%
558604 1
0.1%

xp
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1225
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean872633.49
Minimum0
Maximum14970539
Zeros224
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:15.200269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12106.25
median63968
Q3828669
95-th percentile4863958.4
Maximum14970539
Range14970539
Interquartile range (IQR)826562.75

Descriptive statistics

Standard deviation1795754.8
Coefficient of variation (CV)2.0578568
Kurtosis12.598926
Mean872633.49
Median Absolute Deviation (MAD)63968
Skewness3.2112612
Sum1.359563 × 109
Variance3.2247352 × 1012
MonotonicityNot monotonic
2023-04-12T16:34:15.512238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 224
 
14.4%
200 14
 
0.9%
100 11
 
0.7%
300 7
 
0.4%
450 5
 
0.3%
1250 4
 
0.3%
650 4
 
0.3%
1350 4
 
0.3%
700 4
 
0.3%
1150 4
 
0.3%
Other values (1215) 1277
82.0%
ValueCountFrequency (%)
0 224
14.4%
50 1
 
0.1%
100 11
 
0.7%
125 1
 
0.1%
150 2
 
0.1%
200 14
 
0.9%
250 1
 
0.1%
260 1
 
0.1%
300 7
 
0.4%
350 3
 
0.2%
ValueCountFrequency (%)
14970539 1
0.1%
14888428 1
0.1%
12192285 1
0.1%
11391186 1
0.1%
11311835 1
0.1%
11080512 1
0.1%
10525046 1
0.1%
9978904 1
0.1%
9310844 1
0.1%
9235667 1
0.1%

scorePerMinute
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct959
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.86883
Minimum0
Maximum413.8
Zeros597
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:15.895103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median56.793995
Q3221.64898
95-th percentile305.2305
Maximum413.8
Range413.8
Interquartile range (IQR)221.64898

Descriptive statistics

Standard deviation116.52668
Coefficient of variation (CV)1.0802628
Kurtosis-1.2358138
Mean107.86883
Median Absolute Deviation (MAD)56.793995
Skewness0.53815459
Sum168059.63
Variance13578.466
MonotonicityNot monotonic
2023-04-12T16:34:16.259446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 597
38.3%
237.84 2
 
0.1%
357.6 2
 
0.1%
316.2 2
 
0.1%
237.4983471 1
 
0.1%
244.9037634 1
 
0.1%
260.1402985 1
 
0.1%
178.6901408 1
 
0.1%
2.852380952 1
 
0.1%
186.2367713 1
 
0.1%
Other values (949) 949
60.9%
ValueCountFrequency (%)
0 597
38.3%
0.026582278 1
 
0.1%
0.050322581 1
 
0.1%
0.073109244 1
 
0.1%
0.139065421 1
 
0.1%
0.195652174 1
 
0.1%
0.399406968 1
 
0.1%
0.474418605 1
 
0.1%
0.567536232 1
 
0.1%
0.569620253 1
 
0.1%
ValueCountFrequency (%)
413.8 1
0.1%
413.3461078 1
0.1%
394.0285714 1
0.1%
392.6 1
0.1%
389.85 1
0.1%
375.4 1
0.1%
372.6 1
0.1%
367.2 1
0.1%
362.015534 1
0.1%
357.6 2
0.1%

shots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct945
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55686.769
Minimum0
Maximum1166620
Zeros600
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:16.622559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1565
Q350781
95-th percentile309596.6
Maximum1166620
Range1166620
Interquartile range (IQR)50781

Descriptive statistics

Standard deviation120281.08
Coefficient of variation (CV)2.1599579
Kurtosis16.293985
Mean55686.769
Median Absolute Deviation (MAD)1565
Skewness3.5256354
Sum86759986
Variance1.4467538 × 1010
MonotonicityNot monotonic
2023-04-12T16:34:16.976430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 600
38.5%
209 2
 
0.1%
566 2
 
0.1%
114 2
 
0.1%
10 2
 
0.1%
6 2
 
0.1%
1565 2
 
0.1%
9 2
 
0.1%
250 2
 
0.1%
206 2
 
0.1%
Other values (935) 940
60.3%
ValueCountFrequency (%)
0 600
38.5%
2 2
 
0.1%
3 2
 
0.1%
4 1
 
0.1%
6 2
 
0.1%
9 2
 
0.1%
10 2
 
0.1%
12 1
 
0.1%
18 1
 
0.1%
26 1
 
0.1%
ValueCountFrequency (%)
1166620 1
0.1%
925621 1
0.1%
888809 1
0.1%
876751 1
0.1%
818392 1
0.1%
787680 1
0.1%
776465 1
0.1%
745789 1
0.1%
743243 1
0.1%
633504 1
0.1%

deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct920
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3875.3979
Minimum0
Maximum67888
Zeros212
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-04-12T16:34:17.378960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median269
Q33698.75
95-th percentile20390.85
Maximum67888
Range67888
Interquartile range (IQR)3684.75

Descriptive statistics

Standard deviation7992.6664
Coefficient of variation (CV)2.0624118
Kurtosis13.98476
Mean3875.3979
Median Absolute Deviation (MAD)269
Skewness3.3393438
Sum6037870
Variance63882717
MonotonicityNot monotonic
2023-04-12T16:34:17.671261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212
 
13.6%
2 44
 
2.8%
4 18
 
1.2%
1 16
 
1.0%
6 15
 
1.0%
10 13
 
0.8%
16 13
 
0.8%
14 12
 
0.8%
5 10
 
0.6%
9 10
 
0.6%
Other values (910) 1195
76.7%
ValueCountFrequency (%)
0 212
13.6%
1 16
 
1.0%
2 44
 
2.8%
3 9
 
0.6%
4 18
 
1.2%
5 10
 
0.6%
6 15
 
1.0%
7 8
 
0.5%
8 7
 
0.4%
9 10
 
0.6%
ValueCountFrequency (%)
67888 1
0.1%
61691 1
0.1%
59707 1
0.1%
56771 1
0.1%
56067 1
0.1%
48819 1
0.1%
48596 1
0.1%
47363 1
0.1%
47229 1
0.1%
45798 1
0.1%

source
URL

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1557
Missing (%)99.9%
Memory size48.9 KiB
https://www.kaggle.com/datasets/aishahakami/call-of-duty-players
 
1
(Missing)
1557 
ValueCountFrequency (%)
https://www.kaggle.com/datasets/aishahakami/call-of-duty-players 1
 
0.1%
(Missing) 1557
99.9%
ValueCountFrequency (%)
https 1
 
0.1%
(Missing) 1557
99.9%
ValueCountFrequency (%)
www.kaggle.com 1
 
0.1%
(Missing) 1557
99.9%
ValueCountFrequency (%)
/datasets/aishahakami/call-of-duty-players 1
 
0.1%
(Missing) 1557
99.9%
ValueCountFrequency (%)
1
 
0.1%
(Missing) 1557
99.9%
ValueCountFrequency (%)
1
 
0.1%
(Missing) 1557
99.9%

Interactions

2023-04-12T16:33:57.006440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:43.722231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:46.456887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:49.308986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:52.060134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:54.529016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:57.251756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:59.984398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:02.589488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:07.839008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:13.448586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:19.738731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:25.327367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:30.753849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:36.009885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:41.136871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:46.440334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:51.545856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:57.292035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:43.851865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:46.604949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:49.424734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:52.185425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:54.654078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:57.445061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:00.138591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:02.915777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:08.171261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:13.767563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:20.092595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:25.667775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:31.072666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:36.284209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:41.473451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:46.637368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:51.858676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:57.641613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:43.977144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:46.757364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:49.567002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:52.325206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:54.794883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:57.575121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:00.268657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:03.198718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:08.488714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:14.081266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:20.452221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:25.969155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:31.312402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:36.609326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:41.771124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:46.928698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:52.162160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:57.941714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:44.086117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:46.961213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:49.729531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:52.489176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:54.915044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:57.706941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:00.455649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:03.470182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:08.832644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:14.403602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:20.921455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:26.289842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:31.578085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:36.931580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:42.053555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:47.184833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:52.428643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:58.230825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:44.222322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:47.134064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:49.825585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:52.593598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:55.037500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:57.842894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:00.589051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:03.710996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:09.152326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:14.699826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:21.241688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:26.622105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:31.841540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:37.210528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:42.312769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:47.454464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:52.712546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:58.510075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:44.342194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:47.271051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:49.973718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:52.696053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:55.205499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:58.021891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:00.720637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:03.945175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:09.409065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:14.984167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:21.493063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:26.896605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:32.115663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:37.465296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:42.586866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:47.681349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:53.019753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:59.040388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:44.494394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:47.427831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:50.182561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:52.843685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:55.343841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:58.214357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:00.862154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:04.254744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:09.768991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:15.374592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:21.807357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:27.195900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:32.477900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:37.782356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:42.855991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:48.009512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:53.328109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:59.367276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:44.631089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:47.574856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:50.364698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:53.028383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:55.479896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:58.466982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:01.029334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:04.505939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:10.123375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:15.744842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:22.033700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:27.527993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:32.712565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:38.056104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:43.173511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:48.292357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:53.613245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:59.652930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:44.765022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:47.722423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:50.490604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:53.132675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:55.624552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:58.589257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:01.152513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-04-12T16:33:10.418129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-04-12T16:33:27.821044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:33.206836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:38.352466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:43.481113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:48.577137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:53.890240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:59.984074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:44.903503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:47.870744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:50.695441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:53.246578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:55.764291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:58.733172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:01.268576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:05.043031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:10.691689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:16.465715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:22.664581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:28.111649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:33.453433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:38.599801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:43.792009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:48.899979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:54.226970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:34:00.319430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:45.053916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:47.990124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:50.848730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:53.329283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:55.882966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:58.876347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:01.384349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:05.248292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:10.986305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:16.808033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:22.930452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:28.359842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:33.713029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:38.902630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:44.024046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:49.162099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:54.556425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:34:00.647895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:45.195831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:48.158069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:51.027799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:53.509255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:56.026989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:59.032511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:01.539909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:05.574282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:11.346740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:17.180754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:23.257138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:28.662809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:34.032257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:39.175125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:44.351887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:49.475660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:54.850556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:34:00.995180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:45.322131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:48.322120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:51.170006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:53.661473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:56.151169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:59.176858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:01.703774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:05.888009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:11.681380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:17.557120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:23.586243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:28.968432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:34.366585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:39.501412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:44.653953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:49.786817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:55.130358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:34:01.322495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:45.470196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:48.455251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:51.318535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:53.826304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:56.287313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:59.280332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:01.841186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:06.382749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:11.964059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:17.945035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:23.848231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:29.264825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:34.590770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:39.769413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:44.959209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:50.105957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:55.477965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:34:01.568906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:45.703534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:48.715388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:51.438705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:53.995700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:56.441264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:59.441123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:01.993764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:06.670557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:12.300712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:18.325737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:24.162488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:29.597754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:34.798191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:40.016017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:45.243584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:50.408822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:55.770939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:34:01.901660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:45.918377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:48.863991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:51.536195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:54.136552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:56.758768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:59.572991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:02.152846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:06.960271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:12.616103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:18.714806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:24.471186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:29.896509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:35.106500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:40.293540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:45.679726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:50.716879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:56.084593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:34:02.157450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:46.075974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:48.973843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:51.660856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:54.228546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:56.902538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:59.688340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:02.283507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:07.259090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:12.871535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:19.043450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:24.755821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:30.128297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:35.381433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:40.571569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:45.922237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:50.952494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:56.354222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:34:02.465895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:46.260044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:49.143356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:51.809570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:54.407763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:57.089887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:32:59.823195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:02.434025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:07.560346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:13.149410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:19.401140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:25.006716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:30.430731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:35.681630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:40.868706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:46.135204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:51.243715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T16:33:56.666059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-04-12T16:34:18.038542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
winskillskdRatiokillstreaklevellossesprestigehitstimePlayedheadshotsaverageTimegamesPlayedassistsmissesxpscorePerMinuteshotsdeaths
wins1.0000.9150.6710.9070.8770.8890.5480.9400.8840.9070.1410.9370.8930.9410.8800.7180.9420.918
kills0.9151.0000.8060.8990.9730.9170.6750.9250.9700.9900.2950.9210.9600.9230.9800.6880.9230.992
kdRatio0.6710.8061.0000.7360.7690.7170.5270.7060.7360.7940.2830.6870.7240.6900.7890.5400.6930.750
killstreak0.9070.8990.7361.0000.8410.9140.5050.9650.8270.8950.0430.9480.9060.9590.8410.7950.9600.889
level0.8770.9730.7690.8411.0000.8760.7330.8620.9810.9720.3710.8630.9270.8600.9910.5580.8610.968
losses0.8890.9170.7170.9140.8761.0000.5630.9340.8670.9160.0830.9480.9230.9310.8730.7520.9320.914
prestige0.5480.6750.5270.5050.7330.5631.0000.5190.7420.6750.4640.5260.6820.5210.7370.2000.5210.678
hits0.9400.9250.7060.9650.8620.9340.5191.0000.8520.9200.0240.9810.9400.9960.8610.8160.9970.921
timePlayed0.8840.9700.7360.8270.9810.8670.7420.8521.0000.9620.4320.8570.9210.8540.9900.5610.8550.978
headshots0.9070.9900.7940.8950.9720.9160.6750.9200.9621.0000.2710.9180.9560.9160.9730.6720.9170.982
averageTime0.1410.2950.2830.0430.3710.0830.4640.0240.4320.2711.0000.0070.1760.0300.412-0.1240.0300.313
gamesPlayed0.9370.9210.6870.9480.8630.9480.5260.9810.8570.9180.0071.0000.9300.9810.8600.7930.9810.920
assists0.8930.9600.7240.9060.9270.9230.6820.9400.9210.9560.1760.9301.0000.9380.9270.7180.9380.960
misses0.9410.9230.6900.9590.8600.9310.5210.9960.8540.9160.0300.9810.9381.0000.8610.8221.0000.923
xp0.8800.9800.7890.8410.9910.8730.7370.8610.9900.9730.4120.8600.9270.8611.0000.5720.8610.977
scorePerMinute0.7180.6880.5400.7950.5580.7520.2000.8160.5610.672-0.1240.7930.7180.8220.5721.0000.8220.692
shots0.9420.9230.6930.9600.8610.9320.5210.9970.8550.9170.0300.9810.9381.0000.8610.8221.0000.923
deaths0.9180.9920.7500.8890.9680.9140.6780.9210.9780.9820.3130.9200.9600.9230.9770.6920.9231.000

Missing values

2023-04-12T16:34:02.979548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-12T16:34:03.827010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namewinskillskdRatiokillstreaklevellossesprestigehitstimePlayedheadshotsaverageTimegamesPlayedassistsmissesxpscorePerMinuteshotsdeathssource
0RggRt45#4697369000.00000001000000.00000000000.00000000https://www.kaggle.com/datasets/aishahakami/call-of-duty-players
1JohniceRex#9176033000.0000000101100707.0000000007000.000000016NaN
2bootybootykill#18920640661.0312500901100321632.000000010483000.000000064NaN
3JNaCo#5244172320.40000001000303.00000000011500.00000005NaN
4gomezyayo_007#6596687020.2000000101100515.00000000010000.000000010NaN
5Brxndoon7-LK#4002715684270111.066743181771011098332136651132.32312958860633053193932335255.67203540365125321NaN
6bdooory_ab#709517141620.63281246205688352.000000468483624485265.5000005404256NaN
7ahevepluto#350530418618980.56962813377251115504853.66666715048839978458269180.379636450893332NaN
8MilkyLemonz-_-#5981249741218031.036658261852911181361244238942.82638986440293272304269370198.84422640859121032NaN
9RPDUNKduo#8122914263490.4440207124099644402.93333315138484472765253.6772735840786NaN
namewinskillskdRatiokillstreaklevellossesprestigehitstimePlayedheadshotsaverageTimegamesPlayedassistsmissesxpscorePerMinuteshotsdeathssource
1548AKR_aaa#54030246823470.94030492891251951534421.8433738323015101277540309.913725202962496NaN
1549MeaslyPanther81#4102876234060.8218621210611946651322.32142928119715957364187.1261549105494NaN
1550bimo09#902516640875300.79987312778283353369412372.67953725917921571881208952296.2002881907219414NaN
1551JermWormy#51254021173457641.0786272028914111128437327271462.655844123271334097488740718214.81943853818542428NaN
1552LokiiFN#2367402000.00000001000101.0000000002000.00000002NaN
1553ImranePROPG#20852150510.43220301001100711171.000000000499350.0000000118NaN
1554Miguel_mor#1492856000.00000001000000.00000000000.00000000NaN
1555Trianthor#3462590000.00000001000000.00000000000.00000000NaN
1556tinytrex594#3976133000.00000001002101.000000017091.20000090NaN
1557TomasFJ_05#27110691350.6862753511101710610.000000111631712639.0000008051NaN